Feature selection plays a crucial role in the rapidly evolving field of data analytics as it identifies the most pertinent features for modeling and analysis, thereby improving the accuracy, interpretability, and efficiency of data analytics tasks by reducing the dimensionality of data. Conventional feature selection techniques frequently rely on optimizing a single criterion, such as accuracy or information gain, to evaluate and select features. Nonetheless, these methods may not capture the complete complexity of real-world problems with multiple and commonly competing objectives. Modern researchers have successfully applied multi-objective optimization techniques to a variety of traditional feature selection algorithms, thereby providing a framework for optimizing feature subsets that strike a proportion between several objectives, such as precision, complexity, interpretability, and robustness. However, it is challenging to determine which algorithm would be the most efficient for a given dataset. The purpose of this chapter is to provide researchers and practitioners with an in-depth examination of various multi-objective feature selection approaches according to their strengths and weaknesses, as well as case studies for each technique, so that these advanced methodologies can be applied in research projects, decision-making processes, and real-world data analytics applications. The aim is to provide insight into the comparative analysis between multi-objective and single-objective feature selection, allowing professionals to gain a deeper comprehension of the trade-offs and benefits of considering multiple objectives. Specific challenges and limitations of multi-objective feature selection are discussed, along with strategies for overcoming obstacles such as managing computational resources and dealing with high-dimensional data.